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UberCloud Publishes Compendium Of Case Studies in Life Sciences

If you are considering moving some of your HPC workload to the Cloud, nothing leads the way like a good set of case studies in your scientific domain. To this end, our good friends at the UberCloud have published a compendium entitled, Exploring Life Sciences in the Cloud. The document includes 36 CFD case studies summarizing HPC Cloud projects that the UberCloud has performed together with the engineering community over the last six years. “From the 220 cloud experiments we have done so far, we selected 15 case studies related to the life sciences. We document the results of these research teams, their applications, findings, challenges, lessons learned, and recommendations.”

The GigaIO FabreX Network – New Frontiers in Networking For Big Data

GigaIO has developed a new whitepaper to describe GigaIO FabreX, a fundamentally new network architecture that integrates computing, storage, and other communication I/O into a single-system cluster network, using industry standard PCIe (peripheral component interconnect express) technology.

Whitepaper: Accelerate Training of Deep Neural Networks with MemComputing

“The paper addresses the inherent limitations associated with today’s most popular gradient-based methods, such as Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent (SGD), which incorporate backpropagation. MemComputing’s approach instead aims towards a more global and parallelized optimization algorithm, achievable through its entirely new computing architecture.”

Compendium of articles published on Numerical Algorithms for HPC Science

The Royal Society Publishing has recently released a special compendium of articles based on a recent scientific discussion meeting with HPC Industry thought leaders. “This issue contains contributions from those who develop and implement numerical algorithms and software libraries – numerical analysts, computer scientists, and high-performance computing researchers – with those who use them in some of today’s most challenging applications.”

vScaler Launches AI Reference Architecture

A new AI reference architecture from vScaler describes how to simplify the configuration and management of software and storage in a cost-effective and easy to use environment. “vScaler – an optimized cloud platform built with AI and Deep Learning workloads in mind – provides you with a production ready environment with integrated Deep Learning application stacks, RDMA accelerated fabric and optimized NVMe storage, eliminating the administrative burden of setting up these complex AI environments manually.”

ECP Report: Advancing Scientific Productivity through Better Scientific Software

The Exascale Computing Project has published a new report to foster and advance software productivity and sustainability for extreme-scale computational science. The report introduces work by the IDEAS-ECP project, explaining its approach, outcomes, and impact of work in partnership with the ECP and broader computational science community. The DOE Exascale Computing Project (ECP) provides a […]

WekaIO Named Major Player in File-Based Storage by IDC MarketScape

IDC MarketScape has recognized WekaIO as a Major Player in this sector. According to the IDC MarketScape 2019, IDC believes that file-based storage (FBS) will continue to evolve to address the needs of traditional and next-generation workloads. The IDC MarketScape noted, “WekaFS was developed from the ground up to utilize the performance of NVMe flash technology to deliver the optimum performance and minimum latency for demanding and unpredictable AI workloads. WekaIO’s customers claim satisfaction and that the offering holds to performance promises made by the vendor.”

The Use of High-Performance Polymers in HPC and Data Center Applications

“Polymer components in liquid cooling systems are attractive for several reasons: they are lightweight, typically less expensive than metal counterparts, and are impervious to corrosion that can render parts inoperable or introduce debris into flow paths. The challenges with many polymers used to date, however, are their abilities to handle high temperatures and physical stressors without deforming, cracking or creeping. These shortcomings become significant when leaks occur, leading to downtime or damage to equipment.”

Deep Learning on Summit Supercomputer Powers Insights for Nuclear Waste Remediation

A research collaboration between LBNL, PNNL, Brown University, and NVIDIA has achieved exaflop (half-precision) performance on the Summit supercomputer with a deep learning application used to model subsurface flow in the study of nuclear waste remediation. Their achievement, which will be presented during the “Deep Learning on Supercomputers” workshop at SC19, demonstrates the promise of physics-informed generative adversarial networks (GANs) for analyzing complex, large-scale science problems.

Intersect360 Research Examines Spending Trends in Machine Learning Market

Intersect360 Research has released a pair of new reports examining major technology trends in AI and machine learning, including the worldwide market, spending trends, and impact on HPC. “Machine learning has been in a very high growth stage,” says Intersect360 Research CEO Addison Snell. “In addition to that $10 billion, many systems not one hundred percent dedicated to machine learning are serving training needs as part of their total workloads, increasing the influence that machine learning has on spending and configuration.”